OPTDOccupational Projections and Training Data (Bureau of Labor Statistics; US DOL)
OPTDOutside Petting the Dog (chat slang)
OPTDOrascom Projects and Touristic Development (Egypt)
OPTDOpel Performance Team Dachau (car club; Dachau, Germany)
OPTDOperational Prognostics Technology Demonstration
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References in periodicals archive ?
The proposed OptD method in new optimal variant can be used not only on the data obtained from ALS, but also for TLS (Terrain Laser Scanning) and other large datasets, e.g.
The OptD-single method enables the reduction of big dataset by means of one optimization criterion The algorithm of OptD method consists of the following steps:
Presented algorithm of OptD method was tested on real ALS data.
TSset with the number N = 108 313 points has been processed by OptD methods, that begins with the determination of optimization criterion.
Before the processing by means of the proposed method OptD had begun, the mean error m0 was calculated for TSset, [m.sub.0] = 0.795 m.
In the course of running the OptD method, the output tolerance 0.250 m was changed to 0.235 m (during iteration) in order to complete the established optimization criterion.
Application of the OptD method selected the optimum solution, which is presented in Figure 6a.
It is important that in applying the OptD method which exactly meets our expectations written by means of optimization criterion.
In this paper a new OptD method in the processing of LiDAR point cloud was proposed and tested.
Innovative OptD method is a simple in application method for data reduction, which takes into account optimization criteria.
The result of the implementation of the OptD method is an optimal dataset that can be used to generate DTM.
The OptD method fulfills all the expectations of reducing the size of the dataset without losing data necessary for the proper DTM generation.